Click on the map to add points. See the console for lat/long output.
View twitterusers.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/usr/bin/python3 | |
import tweepy | |
import csv | |
consumer_key = "" | |
consumer_secret = "" | |
access_token = "" | |
access_token_secret = "" |
View metro.R
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidycensus) | |
library(tidyverse) | |
# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html) | |
# census_api_key("YOUR KEY HERE", install = TRUE) | |
get_acs(geography = "metropolitan statistical area/micropolitan statistical area", | |
variables = "DP03_0021PE", | |
summary_var = "B01003_001", | |
survey = "acs1", |
View census_cleanup.R
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(tidycensus) | |
# My recommendation is to use the tidycensus library to make getting this data | |
# easier than reading in the data from the Census website. | |
# | |
# Before you can begin, you'll need to get an API key from the Census Bureau. | |
# You can acquire one here: | |
# | |
# Once you have the API key, run the following in RStudio: |
View messy.R
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(charlatan) | |
library(salty) | |
library(magrittr) | |
library(readr) | |
messydata <- ch_generate('name','job','phone_number', n = 200) | |
messydata <- messydata %>% | |
mutate(job = salt_capitalization(job)) %>% | |
mutate(phone_number = salt_na(phone_number)) %>% |
View frequency_to_list.R
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(readxl) | |
data <- readxl::read_xlsx("data.xlsx") | |
reshaped <- data %>% gather(word, freq, 2:21) | |
reshaped <- reshaped %>% drop_na() | |
cleaned <- reshaped %>% | |
uncount(freq) |
View geofilter.R
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(tidyverse) | |
library(maps) | |
library(mapdata) | |
data <- read_csv("~/Desktop/nplsuperfund.csv") | |
names(data) <- c("lat","lon","date") | |
# Filter down to USA extent to remove extraneous points | |
tidy <- data %>% | |
filter(lat < -67, lat > -125) %>% |
View hex_logo.R
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
library(hexSticker) | |
library(tidyverse) | |
library(tidycensus) | |
library(sf) | |
library(viridis) | |
options(tigris_use_cache = TRUE) | |
nebraska_raw <- get_acs(state = "NE", | |
geography = "tract", |
View pandas.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# Unique values in a dataframe column | |
df['column_name'].unique() | |
# Grab dataframe rows where column = value | |
df = df.loc[df.column == 'some_value'] | |
# Grab dataframe rows where column value is present in a list | |
value_list = ['value1', 'value2', 'value3'] | |
df = df.loc[:,df.columns.isin(valuelist)] | |
# or grab rows where a value is not present in a list |
View index.html
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
<!DOCTYPE html> | |
<head> | |
<meta charset="utf-8"> | |
<script src="https://d3js.org/d3.v4.min.js"></script> | |
<script src="http://www.webglearth.com/v2/api.js"></script> | |
<script> | |
function map() { | |
var options = { zoom: 1.5, position: [47.19537,8.524404] }; | |
var earth = new WE.map('earth_div', options); |
NewerOlder